langsmith-observability

LLM observability provides run-level visibility into calls, chains, and tools to diagnose failures. Tracing captures execution paths, enabling developers to debug prompts, validate outputs, and optimize performance across development and production environments.

To start tracing and evaluating, install the LangSmith SDK and integrate the client into your application. You must configure API keys and endpoints to begin capturing runs, traces, and datasets for monitoring model behavior.

Yes, you can evaluate LLM outputs systematically by building test sets and datasets. This allows you to run evaluations against captured data to validate model behavior, test prompt changes, and ensure output quality before production deployment.

LangSmith observability supports monitoring production deployments by tracking metrics such as latency and costs. It enables teams to capture and evaluate model behavior in real-time, ensuring optimal performance and diagnosing issues in production environments.

Yes, LangSmith observability supports integrations with LangChain and OpenTelemetry. You can use hub prompts and integrate tracing capabilities within your existing workflows to capture and evaluate LLM behavior across compatible frameworks.

Without end-to-end observability, diagnosing LLM behavior is limited because you lack run-level visibility for calls and tools. You cannot systematically evaluate outputs against datasets or monitor production metrics, making failure diagnosis and optimization difficult.